Clinical lung cancer diagnosis depends upon the pathologist's interpretation of morphological features of histological and cytological specimens. These interpretations may be assisted by detection of cancer cell related moleculars to improve sensitivity and specificity of the diagnostic techniques.
Molecular methods, including application of cancer-specific markers, may prove to be complementary to cytology diagnosis. Quantitative evaluation of lung carcinomas with cytoplasmic markers has been successfully investigated. Imaging technology provides an objective way for the quantitative analysis of tumor cells morphology. Current imaging practices are mostly manual, time-consuming, and tedious, yielding subjective and imprecise results. In order to improve the situation, many methods for computer-aided diagnosis of cell images have been designed. The methods including common commercial tools for computer aided morphologic image analysis utilizing region-based methods and threshold based methods. Region-based methods separate the object from the background by region growing, region splitting, and merging of regions to segment the image. Threshold-based segmentation is a simpler method based on single pixel classification. In threshold-based segmentation, a feature value such as gray level is associated with each pixel and this value is compared to the threshold to classify a pixel as an object or background. Determination of the threshold is critical; a simple method is to select the threshold as determined by a histogram. More sophisticated versions of threshold-based segmentation are also known in the art. The problem with these approaches is that they employ only local (single pixel) information.
Cellular images share the following characteristics:    (1) Poor contrast: Object (cell) gray levels may be close to that of the background.    (2) Many cluttered objects (cells) in a single scene: This high number of occluding objects makes image segmentation difficult.    (3) Low quality: Traditional staining techniques introduce a lot of inhomogeneity into the images, where not all of the parts of the same tissue are equally stained.
Accordingly, what is needed in the art is an improved computer-aided pathological diagnosis system and method for the classification of cancer cells in a tissue specimen that overcomes the deficiencies of the prior art systems and methods.